In High Energy Physics, detailed calorimeter simulations and reconstructions are essential for accurate energy measurements and particle identification, but their high granularity makes them computationally expensive. Developing data-driven techniques capable of recovering fine-grained information from coarser readouts, a task known as calorimeter superresolution, offers a promising way to reduce both computational and hardware costs while preserving detector performance. This thesis investigates whether a generative model originally designed for fast simulation can be effectively applied to calorimeter superresolution. Specifically, the model proposed in arXiv:2308.11700 is re-implemented independently and trained on the CaloChallenge 2022 dataset based on the Geant4 Par04 calorimeter geometry. Finally, the model's performance is assessed through a rigorous statistical evaluation framework, following the methodology introduced in arXiv:2409.16336, to quantitatively test its ability to reproduce the reference distributions.

Deep learning methods have gained popularity in high energy physics for fast modeling of particle showers in detectors.

Anomaly detection in multivariate time series is crucial to ensure the quality of data coming from a physics experiment. Accurately identifying the moments when unexpected errors or defects occur is essential, yet challenging due to scarce labels, unknown anomaly types, and complex correlations across dimensions. To address the scarcity and unreliability of labelled data, we use the Lorenzetti Simulator to generate synthetic events with injected calorimeter anomalies. We then assess the sensitivity of several time series anomaly detection methods, including transformer-based and other deep learning models. The approach employed here is generic and applicable to different detector designs and defects.

The physics programs of current and future collider experiments necessitate the development of surrogate simulators for calorimeter showers. While much progress has been made in the development of generative models for this task, they have typically been evaluated in simplified scenarios and for single particles. This is particularly true for the challenging task of highly granular calorimeter simulation. For the first time, this work studies the use of highly granular generative calorimeter surrogates in a realistic simulation application. We introduce DDML, a generic library which enables the combination of generative calorimeter surrogates with realistic detectors implemented using the DD4hep toolkit. We compare two different generative models - one operating on a regular grid representation, and the other using a less common point cloud approach. In order to disentangle methodological details from model performance, we provide comparisons to idealized simulators which directly sample representations of different resolutions from the full simulation ground-truth. We then systematically evaluate model performance on post-reconstruction benchmarks for electromagnetic shower simulation. Beginning with a typical single particle study, we introduce a first multi-particle benchmark based on di-photon separations, before studying a first full-physics benchmark based on hadronic decays of the tau lepton. Our results indicate that models operating on a point cloud can achieve a favorable balance between speed and accuracy for highly granular calorimeter simulation compared to those which operate on a regular grid representation.

Deep learning methods have gained popularity in high energy physics for fast modeling of particle showers in detectors.

Building generative surrogates for expensive event generation and simulation tasks is a key step in enabling the physics program of the high-luminosity LHC (HL-LHC) and future collider studies [1-3]. As experiments in high energy physics push the boundaries of luminosity resulting in ever increasing event rates, the computational demand of high-precision Monte Carlo (MC) simulations is growing to the point where it will soon surpass available computational resources [4]. Generative models offer a promising solution to this challenge, potentially reducing the immense computational load required for these simulations. This has led to substantial research into the development of machine-learning architectures tailored for more efficient and accurate detector simulation [5, 6]. Examples include generative adversarial networks (GANs) [7-18], variational autoencoders (V AEs) and their variants [18-24], normalizing flows and various types of diffusion models [23, 25-45], as well as generative pre-trained transformer (GPT) style models [46]. The combination of a diffusion model with a transformer architecture, known as diffusion transformers [47, 48], has been used in high-energy physics for jet generation [45, 49-52]. The majority of these studies have focused on simulating electromagnetic showers, for a recent review see [53].

We introduce a novel approach for end-to-end black-box optimization of high energy physics (HEP) detectors using local deep learning (DL) surrogates. These surrogates approximate a scalar objective function that encapsulates the complex interplay of particle-matter interactions and physics analysis goals. In addition to a standard reconstruction-based metric commonly used in the field, we investigate the information-theoretic metric of mutual information. Unlike traditional methods, mutual information is inherently task-agnostic, offering a broader optimization paradigm that is less constrained by predefined targets. We demonstrate the effectiveness of our method in a realistic physics analysis scenario: optimizing the thicknesses of calorimeter detector layers based on simulated particle interactions. The surrogate model learns to approximate objective gradients, enabling efficient optimization with respect to energy resolution. Our findings reveal three key insights: (1) end-to-end black-box optimization using local surrogates is a practical and compelling approach for detector design, providing direct optimization of detector parameters in alignment with physics analysis goals; (2) mutual information-based optimization yields design choices that closely match those from state-of-the-art physics-informed methods, indicating that these approaches operate near optimality and reinforcing their reliability in HEP detector design; and (3) information-theoretic methods provide a powerful, generalizable framework for optimizing scientific instruments. By reframing the optimization process through an information-theoretic lens rather than domain-specific heuristics, mutual information enables the exploration of new avenues for discovery beyond conventional approaches.

Deep learning techniques have improved performance beyond conventional methods in a wide variety of tasks. However, for neural networks in particular, it is not straightforward to assign network-induced uncertainty on their output as a function of network architecture, training algorithm, and initialization [1]. One approach to uncertainty quantification (UQ) is to treat any individual network as a draw from an ensemble, and identify the systematic uncertainty with the variance in the neural network outputs over the ensemble [2, 3]. This variance can certainly be measured empirically by training a large ensemble of networks, but it would be advantageous to be able to predict it from first principles. This is possible in the infinite-width limit of multi-layer perceptron (MLP) architectures, where the statistics of the network outputs after training are Gaussian with mean and variance determined by the neural tangent kernel (NTK) [4-6]. For realistic MLPs with large but finite width n, one can compute corrections to this Gaussian distribution that are perturbative in 1/n [7].

Deep learning techniques have evolved rapidly in recent years, significantly impacting various scientific fields, including experimental particle physics. To effectively leverage the latest developments in computer science for particle physics, a strengthened collaboration between computer scientists and physicists is essential. As all machine learning techniques depend on the availability and comprehensibility of extensive data, clear data descriptions and commonly used data formats are prerequisites for successful collaboration. In this study, we converted open data from the Large Hadron Collider, recorded in the ROOT data format commonly used in high-energy physics, to pandas DataFrames, a well-known format in computer science. Additionally, we provide a brief introduction to the data's content and interpretation. This paper aims to serve as a starting point for future interdisciplinary collaborations between computer scientists and physicists, fostering closer ties and facilitating efficient knowledge exchange.

A foundation model is a machine learning model that has been pre-trained on a large amount of data, Machine learning (ML) methods have been a common and can then be fine-tuned for different downstream ingredient in particle physics research for a long tasks [61]. The idea behind utilizing pre-trained time, with neural networks being applied to object models is that their outputs can significantly enhance identification already in analyses at LEP [1]. Since the performance of downstream tasks, yielding then, the range of applications has grown drastically, better results than if the model were to be trained with ML methods being developed and used for from scratch. While the models mentioned above example in tagging [2-4], anomaly detection [5-8], have focused on exploring different tasks in specific individual reconstruction stages like particle tracking subdomains, like jet physics, a more ambitious goal [9-11] or even full event interpretation and reconstruction eventually would be to develop a foundation model [12]. Another important use case for for all tasks in all subdomains, including for example ML in high energy physics (HEP) is detector simulation.